Beyond Clickstream: A Critical Synthesis of Intelligent Recommendation Systems for Digital Education Platforms
Keywords:
Recommender systems, digital education, personalised learning, collaborative filtering, knowledge tracing, online learning platformsAbstract
The proliferation of digital education platforms has generated an urgent need for intelligent recommendation systems capable of guiding learners through increasingly complex information environments. Despite substantial investment in algorithmic development and a growing body of empirical research, the field of educational recommendation remains characterized by fragmented findings, undertheorized design choices, and a persistent gap between technical innovation and pedagogical effectiveness. This paper presents a systematic critical synthesis of the literature on intelligent recommendation systems for digital education platforms, examining 84 peer reviewed studies published between 2019 and 2026. We identify three fundamental tensions that structure the field: the tradeoff between accuracy and diversity in recommendation objectives, the challenge of distinguishing genuine learning preferences from behavioral noise, and the unresolved relationship between algorithmic personalisation and learner autonomy. Our analysis reveals that the predominant technical paradigm, which borrows heavily from e commerce recommendation frameworks, systematically undervalues the distinctive features of educational contexts, including the importance of knowledge gaps as drivers of learning motivation, the sequential dependencies inherent in skill acquisition, and the normative goals of education beyond mere engagement maximisation. We propose an integrative framework that reconceptualises educational recommendation as a form of pedagogical mediation rather than preference satisfaction, with implications for system design, evaluation methodologies, and future research priorities.References
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